Results 1 to 10 of about 165,702 (282)
DENOISING OF 3D POINT CLOUDS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 A method to remove random errors from 3D point clouds is proposed. It is based on the estimation of a local geometric descriptor of each point. For mobile mapping LiDAR and airborne LiDAR, a combined standard mesurement uncertainty of the LiDAR system ...
E. Mugner, N. Seube
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Pruning Points Detection of Sweet Pepper Plants Using 3D Point Clouds and Semantic Segmentation Neural Network [PDF]
Sensors, 2023 Automation in agriculture can save labor and raise productivity. Our research aims to have robots prune sweet pepper plants automatically in smart farms.
Truong Thi Huong Giang, Young-Jae Ryoo
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PFuji-Size dataset: A collection of images and photogrammetry-derived 3D point clouds with ground truth annotations for Fuji apple detection and size estimation in field conditions [PDF]
Data in Brief, 2021 The PFuji-Size dataset is comprised of a collection of 3D point clouds of Fuji apple trees (Malus domestica Borkh. cv. Fuji) scanned at different maturity stages and annotated for fruit detection and size estimation.
Jordi Gené-Mola +4 more
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Geological surface reconstruction from 3D point clouds. [PDF]
MethodsX, 2021 The numerical simulation of phenomena such as subsurface fluid flow or rock deformations are based on geological models, where volumes are typically defined through stratigraphic surfaces and faults, which constitute the geometric constraints, and then discretized into blocks to which relevant petrophysical or stress-strain properties are assigned ...
Serazio C +3 more
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Point Siamese Network for Person Tracking Using 3D Point Clouds. [PDF]
Sensors (Basel), 2019 Person tracking is an important issue in both computer vision and robotics. However, most existing person tracking methods using 3D point cloud are based on the Bayesian Filtering framework which are not robust in challenging scenes. In contrast with the filtering methods, in this paper, we propose a neural network to cope with person tracking using ...
Cui Y, Fang Z, Zhou S.
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SIMULATING LIDAR TO CREATE TRAINING DATA FOR MACHINE LEARNING ON 3D POINT CLOUDS [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 3D point clouds represent an essential category of geodata used in a variety of geoinformation applications. Typically, these applications require additional semantics to operate on subsets of the data like selected objects or surface categories. Machine
J. Hildebrand +3 more
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IMAGE TO POINT CLOUD TRANSLATION USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK FOR AIRBORNE LIDAR DATA [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021 This study introduces a novel image to a 3D point-cloud translation method with a conditional generative adversarial network that creates a large-scale 3D point cloud.
T. Shinohara, H. Xiu, M. Matsuoka
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3D Point Cloud Compression [PDF]
The 24th International Conference on 3D Web Technology, 2019 In recent years, 3D point clouds have enjoyed a great popularity for representing both static and dynamic 3D objects. When compared to 3D meshes, they offer the advantage of providing a simpler, denser and more close-to-reality representation. However, point clouds always carry a huge amount of data.
Chao Cao, Marius Preda, Titus Zaharia
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Remote Sensing, 2023 Change detection (CD) is a technique widely used in remote sensing for identifying the differences between data acquired at different times. Most existing 3D CD approaches voxelize point clouds into 3D grids, project them into 2D images, or rasterize ...
Ming Han +3 more
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Learning Multiview 3D Point Cloud Registration [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 CVPR2020 - Camera ...
Gojcic, Zan +4 more
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